Energy Efficient Inverse Power Control for a Cognitive Radio Link

In this paper, a novel adaptive energy and spectrum efficient inverse power control method that is based on the truncated filtered-x LMS (FxLMS) algorithm is introduced. By truncated power control we mean power control where transmission is interrupted if the channel state deteriorates to bad enough. Inverse power control minimizes the interference that a cognitive radio (CR) creates to licensed users and allows more users to share the spectrum. To further reduce the transmission power and consequently the interference, truncation in power control is used. The performance of the system is improved and the amount of needed transmitted energy is smaller. Based on numerical analysis this new method offers energy efficient transmission, helps to minimize interference to the primary users, and allows even more users to share the same spectrum.

[1]  Anahí Gallardo Velázquez,et al.  Conference , 1969, Journal of Neuroscience Methods.

[2]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[3]  Jin-Fu Chang,et al.  A strength and SIR combined adaptive power control for CDMA mobile radio channels , 1996, Proceedings of ISSSTA'95 International Symposium on Spread Spectrum Techniques and Applications.

[4]  Andrea J. Goldsmith,et al.  Truncated power control in code-division multiple-access communications , 2000, IEEE Trans. Veh. Technol..

[5]  Jin-Fu Chang,et al.  A strength-and-SIR-combined adaptive power control for CDMA mobile radio channels , 1999 .

[6]  Philip Harley,et al.  Short distance attenuation measurements at 900 MHz and 1.8 GHz using low antenna heights for microcells , 1989, IEEE J. Sel. Areas Commun..

[7]  A. P. Hulbert.  Spectrum sharing through beacons , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[8]  A. Mammela,et al.  Adaptive Inverse Power Control Using an FxLMS Algorithm , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[9]  Marko Höyhtyä,et al.  Normalization of a Fading Channel , 2006 .

[10]  R. Tandra,et al.  Fundamental limits on detection in low SNR under noise uncertainty , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[11]  Christodoulou.  Antennas and Propagation for Wireless Communication , 2006 .

[12]  Maria Domenica Di Benedetto,et al.  Introducing consciousness in UWB networks by hybrid modelling of admission control , 2006, Mob. Networks Appl..

[13]  Andrea J. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1997, IEEE Trans. Commun..

[14]  Simon R. Saunders,et al.  Antennas and Propagation for Wireless Communication Systems , 1999 .

[15]  Mohamed-Slim Alouini,et al.  Area spectral efficiency of cellular mobile radio systems , 1999 .

[16]  N. Hoven,et al.  Power scaling for cognitive radio , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[17]  Marko Höyhtyä,et al.  Spectrum Awareness: Techniques and Challenges for Active Spectrum Sensing , 2007 .

[18]  A. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1996, Proceedings of Vehicular Technology Conference - VTC.

[19]  A. Goldsmith,et al.  Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques , 1999, IEEE Transactions on Vehicular Technology.

[20]  Semyon M. Meerkov,et al.  Optimal, suboptimal, and adaptive threshold policies for power efficiency of wireless networks , 2005, IEEE Transactions on Information Theory.

[21]  Mansour A. Aldajani,et al.  Adaptive predictive power control for the uplink channel in DS-CDMA cellular systems , 2003, IEEE Trans. Veh. Technol..

[22]  William A. Arbaugh,et al.  Dynamic spectrum access in cognitive radio networks , 2006 .

[23]  Klein S. Gilhousen,et al.  On the system design aspects of code division multiple access (CDMA) applied to digital cellular and personal communications networks , 1991, [1991 Proceedings] 41st IEEE Vehicular Technology Conference.

[24]  Xiongwen Zhao,et al.  Characterization of Doppler spectra for mobile communications at 5.3 GHz , 2003, IEEE Trans. Veh. Technol..

[25]  Bernard Widrow.  Adaptive inverse control , 1990, Defense, Security, and Sensing.

[26]  Scott C. Douglas.  Fast implementations of the filtered-X LMS and LMS algorithms for multichannel active noise control , 1999, IEEE Trans. Speech Audio Process..

[27]  Rahul Tandra,et al.  Fundamental limits on detection in low SNR , 2005 .

[28]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.